Intelligent Image Analysis for Drug Discovery
Biocellvia proposes a new way to evaluate candidate molecules in preclinical assays. Drug development process takes around ten years and costs around one billion euros. Preclinical research is a key challenge for health industry. Biocellvia has developed a unique imaging technology that removes any variability and brings them optimal accuracy to get quantitative data.
Biocellvia has developed digital image analysis solutions for pre-clinical trials. It's a new way to elaborate your candidate molecules thanks to machine-learning based programs.
Idiopathic Pulmonary Fibrosis
COPD - Emphysema
COPD - Small Airway Remodelling (SAR)
Respiratory in vitro 3D model
We have developed digital image analysis solutions based on machine-learning in order to evaluate lead molecules during pre-clinical assays.
Conventional methods had major limits and laborious to use. I understood straight away the advantage of numerical imaging for the industry. Our proprietary technology is based on machine-learning and fully-automated imaging analysis to optimize compound validation assay. This technology applicable to different pathologies, for a clearer decision-making.
This is a breakthrough innovation for pharmaceutical industry.
Our digital technology enables to evaluate candidate molecules during pre-clinical assays. These numerical tests have full benefits :
- Independent-observer removing any variability
- Optimal accuracy to get quantitative data
- Machine-learning based programs
- Our technology reduces delays and costs
Our research & development process enables us to propose available tests for more and more pathologies
Our compound validation assays are machine-learning based programs. We have developed artificial intelligence in order to improve studies' results in pre-clinical assays. Our tests are fully-automated and independent-observer.
The aim is to improve the accuracy and reliability of scientists' analysis and as a result your decision-making while respecting specific processes.